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dc.contributor.authorHébert-Losier, Kimen_NZ
dc.contributor.authorHanzlíková, Ivanaen_NZ
dc.contributor.authorZheng, Chenen_NZ
dc.contributor.authorStreeter, Leeen_NZ
dc.contributor.authorMayo, Michaelen_NZ
dc.date.accessioned2021-11-28T22:34:35Z
dc.date.available2021-11-28T22:34:35Z
dc.date.issued2020en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/14645
dc.description.abstractThe Landing Error Scoring System (LESS) is an injury-risk screening tool used in sports; but scoring is time consuming, clinician-dependent, and generally inaccessible outside of elite sports. Our aim is to evidence that LESS scores can be automated using deep-learning-based computer vision combined with machine learning and compare the accuracy of LESS predictions using different video cropping and machine learning methods. Two-dimensional videos from 320 double-leg drop-jump landings with known LESS scores were analysed in OpenPose. Videos were cropped to key frames manually (clinician) and automatically (computer vision), and 42 kinematic features were extracted. A series of 10 × 10-fold cross-validation experiments were applied on full and balanced datasets to predict LESS scores. Random forest for regression outperformed linear and dummy regression models, yielding the lowest mean absolute error (1.23) and highest correlation (r = 0.63) between manual and automated scores. Sensitivity (0.82) and specificity (0.77) were reasonable for risk categorization (high-risk LESS ≥ 5 errors). Experiments using either a balanced (versus unbalanced) dataset or manual (versus automated) cropping method did not improve predictions. Further research on the automation would enhance the strength of the agreement between clinical and automated scores beyond its current levels, enabling quasi real-time scoring.
dc.format.mimetypeapplication/pdf
dc.language.isoenen_NZ
dc.publisherMDPIen_NZ
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
dc.subjectScience & Technologyen_NZ
dc.subjectPhysical Sciencesen_NZ
dc.subjectTechnologyen_NZ
dc.subjectChemistry, Multidisciplinaryen_NZ
dc.subjectEngineering, Multidisciplinaryen_NZ
dc.subjectMaterials Science, Multidisciplinaryen_NZ
dc.subjectPhysics, Applieden_NZ
dc.subjectChemistryen_NZ
dc.subjectEngineeringen_NZ
dc.subjectMaterials Scienceen_NZ
dc.subjectPhysicsen_NZ
dc.subjectanterior cruciate ligamenten_NZ
dc.subjectautomationen_NZ
dc.subjectdrop jumpen_NZ
dc.subjectinjury risken_NZ
dc.subjectdeep learningen_NZ
dc.subjectmachine learningen_NZ
dc.subjectmovement screenen_NZ
dc.subjectOpenPoseen_NZ
dc.subjectPREDICT INJURYen_NZ
dc.subjectANTERIORen_NZ
dc.subjectREHABILITATIONen_NZ
dc.subjectPREVENTIONen_NZ
dc.subjectTOOLen_NZ
dc.subjectRELIABILITYen_NZ
dc.subjectTECHNOLOGYen_NZ
dc.subjectAGREEMENTen_NZ
dc.subjectSPORTSen_NZ
dc.subjectRISKen_NZ
dc.titleThe 'DEEP' Landing Error Scoring Systemen_NZ
dc.typeJournal Article
dc.identifier.doi10.3390/app10030892en_NZ
dc.relation.isPartOfApplied Sciencesen_NZ
pubs.elements-id250820
pubs.issue3en_NZ
pubs.publication-statusPublisheden_NZ
pubs.volume10en_NZ
dc.identifier.eissn2076-3417en_NZ
uow.identifier.article-noARTN 892


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